code: def continuous_mlp_baseline(ctxt, env_id, seed):
      """Create Continuous MLP Baseline on TF-PPO.
  
      Args:
          ctxt (ExperimentContext): The experiment configuration used by
              :class:`~Trainer` to create the :class:`~Snapshotter`.
          env_id (str): Environment id of the task.
          seed (int): Random positive integer for the trial.
  
      """
      deterministic.set_seed(seed)
  
      with TFTrainer(ctxt) as trainer:
          env = normalize(GymEnv(env_id))
  
          policy = GaussianLSTMPolicy(
              env_spec=env.spec,
              hidden_dim=hyper_params["policy_hidden_sizes"],
              hidden_nonlinearity=hyper_params["hidden_nonlinearity"],
          )
  
          baseline = ContinuousMLPBaseline(
              env_spec=env.spec,
              hidden_sizes=(64, 64),
          )
  
          sampler = RaySampler(
              agents=policy,
              envs=env,
              max_episode_length=env.spec.max_episode_length,
              is_tf_worker=True,
          )
  
          algo = PPO(
              env_spec=env.spec,
              policy=policy,
              baseline=baseline,
              sampler=sampler,
              discount=hyper_params["discount"],
              gae_lambda=hyper_params["gae_lambda"],
              lr_clip_range=hyper_params["lr_clip_range"],
              entropy_method=hyper_params["entropy_method"],
              policy_ent_coeff=hyper_params["policy_ent_coeff"],
              optimizer_args=dict(
                  batch_size=32,
                  max_optimization_epochs=10,
                  learning_rate=1e-3,
              ),
              center_adv=hyper_params["center_adv"],
              stop_entropy_gradient=True,
          )
  
          trainer.setup(algo, env)
          trainer.train(
              n_epochs=hyper_params["n_epochs"],
              batch_size=hyper_params["n_exploration_steps"],
          )
  
file_path: garage/benchmarks/src/garage_benchmarks/experiments/baselines/continuous_mlp_baseline.py
imports: import tensorflow
  from garage import wrap_experiment
  from garage.envs import GymEnv, normalize
  from garage.experiment import deterministic
  from garage.sampler import RaySampler
  from garage.tf.algos import PPO
  from garage.tf.baselines import ContinuousMLPBaseline
  from garage.tf.policies import GaussianLSTMPolicy
  from garage.trainer import TFTrainer
label_desc: Use image node_id 1 for calling a global function with example usage: continuous_mlp_baseline(ctxt, env_id, seed) without return types